Covid19

From epidemic to pandemic

In December 2019, COVID-19 coronavirus was first identified in the Wuhan region of China. By March 11, 2020, the World Health Organization (WHO) categorized the COVID-19 outbreak as a pandemic. A lot has happened in the months in between with major outbreaks in Iran, South Korea, and Italy.

We know that COVID-19 spreads through respiratory droplets, such as through coughing, sneezing, or speaking. But, how quickly did the virus spread across the globe? And, can we see any effect from country-wide policies, like shutdowns and quarantines?

Please note that information and data regarding COVID-19 is frequently being updated. The data used in this project was pulled on March 17, 2020, and should not be considered to be the most up to date data available.

The coronavirus dataset

library(coronavirus)
data(coronavirus)

The dataset has following fields:

• date - The date of the summary
• province - The province or state, when applicable
• country - The country or region name
• Lat - Latitude point
• Long - Longitude point
• type - the type of case (i.e., confirmed, death)
• cases - the number of daily cases (corresponding to the case type)

head(coronavirus)
#>         date province     country lat long      type cases
#> 1 2020-01-22          Afghanistan  33   65 confirmed     0
#> 2 2020-01-23          Afghanistan  33   65 confirmed     0
#> 3 2020-01-24          Afghanistan  33   65 confirmed     0
#> 4 2020-01-25          Afghanistan  33   65 confirmed     0
#> 5 2020-01-26          Afghanistan  33   65 confirmed     0
#> 6 2020-01-27          Afghanistan  33   65 confirmed     0
str(coronavirus)
#> 'data.frame':    87808 obs. of  7 variables:
#>  $ date    : Date, format: "2020-01-22" "2020-01-23" ...
#>  $ province: chr  "" "" "" "" ...
#>  $ country : chr  "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
#>  $ lat     : num  33 33 33 33 33 33 33 33 33 33 ...
#>  $ long    : num  65 65 65 65 65 65 65 65 65 65 ...
#>  $ type    : chr  "confirmed" "confirmed" "confirmed" "confirmed" ...
#>  $ cases   : int  0 0 0 0 0 0 0 0 0 0 ...

Querying and analyzing the coronavirus dataset

library(dplyr)
library(tidyr)
library(plotly)
library(DT)

Cases summary

total_cases <- coronavirus %>%
  group_by(type) %>%
  summarise(cases = sum(cases)) %>%
  mutate(type = factor(type, levels = c("confirmed", "death", "recovered")))

total_cases
#> # A tibble: 3 x 2
#>   type        cases
#>   <fct>       <int>
#> 1 confirmed 4261747
#> 2 death      291942
#> 3 recovered 1493414

The following plot presents the cases (active, recovered, and death) distribution over time:

coronavirus %>%
  group_by(type, date) %>%
  summarise(total_cases = sum(cases)) %>%
  pivot_wider(names_from = type, values_from = total_cases) %>%
  arrange(date) %>%
  mutate(active = confirmed - death - recovered) %>%
  mutate(active_total = cumsum(active),
                recovered_total = cumsum(recovered),
                death_total = cumsum(death)) %>%
  plot_ly(x = ~ date,
                  y = ~ active_total,
                  name = 'Active', 
                  fillcolor = '#1f77b4',
                  type = 'scatter',
                  mode = 'none', 
                  stackgroup = 'one') %>%
  add_trace(y = ~ death_total, 
             name = "Death",
             fillcolor = '#E41317') %>%
  add_trace(y = ~recovered_total, 
            name = 'Recovered', 
            fillcolor = 'forestgreen') %>%
  layout(title = "Distribution of Covid19 Cases Worldwide",
         legend = list(x = 0.1, y = 0.9),
         yaxis = list(title = "Number of Cases"),
         xaxis = list(title = "Source: Johns Hopkins University Center for Systems Science and Engineering"))

Top effected countries

The next table provides an overview of the ten countries with the highest confirmed cases. We will use the datatable function from the DT package to view the table:

confirmed_country <- coronavirus %>% 
  filter(type == "confirmed") %>%
  group_by(country) %>%
  summarise(total_cases = sum(cases)) %>%
  mutate(perc = total_cases / sum(total_cases)) %>%
  arrange(-total_cases)
confirmed_country %>%
  head(10) %>%
  datatable(rownames = FALSE,
            colnames = c("Country", "Cases", "Perc of Total")) %>%
  formatPercentage("perc", 2)

The next plot summarize the distribution of confirmed cases by country:

conf_df <- coronavirus %>% 
  filter(type == "confirmed") %>%
  group_by(country) %>%
  summarise(total_cases = sum(cases)) %>%
  arrange(-total_cases) %>%
  mutate(parents = "Confirmed") %>%
  ungroup() 
  
  plot_ly(data = conf_df,
          type= "treemap",
          values = ~total_cases,
          labels= ~ country,
          parents=  ~parents,
          domain = list(column=0),
          name = "Confirmed",
          textinfo="label+value+percent parent")

Death rates

Similarly, we can use the pivot_wider function from the tidyr package (in addition to the dplyr functions we used above) to get an overview of the three types of cases (confirmed, recovered, and death). We then will use it to derive the recovery and death rate by country. As for most of the countries, there is not enough information about the results of the confirmed cases, we will filter the data for countries with at least 25 confirmed cases and above:

coronavirus %>% 
  filter(country != "Others") %>%
  group_by(country, type) %>%
  summarise(total_cases = sum(cases)) %>%
  pivot_wider(names_from = type, values_from = total_cases) %>%
  arrange(- confirmed) %>%
  filter(confirmed >= 25) %>%
  mutate(death_rate = death / confirmed)  %>%
  datatable(rownames = FALSE,
            colnames = c("Country", "Confirmed","Death", "Death Rate")) %>%
   formatPercentage("death_rate", 2)

Note that it will be misleading to make any conclusion about the recovery and death rate. As there is no detail information about:

• There is no measurement between the time a case was confirmed and recovery or death. This is not an apple to apple comparison, as the outbreak did not start at the same time in all the affected countries.
• As age plays a critical role in the probability of survival from the virus, we cannot make a comparison between different cases without having more demographic information.

Diving into China

The following plot describes the overall distribution of the total confirmed cases in China by province:

coronavirus %>% 
  filter(country == "China",
         type == "confirmed") %>%
  group_by(province, type) %>%
  summarise(total_cases = sum(cases)) %>%  
  pivot_wider(names_from = type, values_from = total_cases) %>%
  arrange(- confirmed) %>%
  plot_ly(labels = ~ province, 
                  values = ~confirmed, 
                  type = 'pie',
                  textposition = 'inside',
                  textinfo = 'label+percent',
                  insidetextfont = list(color = '#FFFFFF'),
                  hoverinfo = 'text',
                  text = ~ paste(province, "<br />",
                                 "Number of confirmed cases: ", confirmed, sep = "")) %>%
  layout(title = "Total China Confirmed Cases Dist. by Province")